Automated data abstraction for quality surveillance and outcome assessment in radiation oncology
Rigorous radiotherapy quality surveillance and comprehensive outcome assessment require electronic capture and automatic ion of clinical, radiation treatment planning, and delivery data. We present the design and implementation framework of an integrated data ion, aggregation, and storage, curation,...
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| Veröffentlicht in: | Journal of Applied Clinical Medical Physics 2021-07, Vol.22 (7), p.177-187 |
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| creator | Kapoor, Rishabh Sleeman, William C. Nalluri, Joseph J. Turner, Paul Bose, Priyankar Cherevko, Andrii Srinivasan, Sriram Syed, Khajamoinuddin Ghosh, Preetam Hagan, Michael Palta, Jatinder R. |
| description | Rigorous radiotherapy quality surveillance and comprehensive outcome assessment require electronic capture and automatic ion of clinical, radiation treatment planning, and delivery data. We present the design and implementation framework of an integrated data ion, aggregation, and storage, curation, and analytics software: the Health Information Gateway and Exchange (HINGE), which collates data for cancer patients receiving radiotherapy. The HINGE software s structured DICOM‐RT data from the treatment planning system (TPS), treatment data from the treatment management system (TMS), and clinical data from the electronic health records (EHRs). HINGE software has disease site‐specific “Smart” templates that facilitate the entry of relevant clinical information by physicians and clinical staff in a discrete manner as part of the routine clinical documentation. Radiotherapy data ed from these disparate sources and the smart templates are processed for quality and outcome assessment. The predictive data analyses are done on using well‐defined clinical and dosimetry quality measures defined by disease site experts in radiation oncology. HINGE application software connects seamlessly to the local IT/medical infrastructure via interfaces and cloud services and performs data extraction and aggregation functions without human intervention. It provides tools to assess variations in radiation oncology practices and outcomes and determines gaps in radiotherapy quality delivered by each provider. |
| doi_str_mv | 10.1002/acm2.13308 |
| format | Article |
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We present the design and implementation framework of an integrated data ion, aggregation, and storage, curation, and analytics software: the Health Information Gateway and Exchange (HINGE), which collates data for cancer patients receiving radiotherapy. The HINGE software s structured DICOM‐RT data from the treatment planning system (TPS), treatment data from the treatment management system (TMS), and clinical data from the electronic health records (EHRs). HINGE software has disease site‐specific “Smart” templates that facilitate the entry of relevant clinical information by physicians and clinical staff in a discrete manner as part of the routine clinical documentation. Radiotherapy data ed from these disparate sources and the smart templates are processed for quality and outcome assessment. The predictive data analyses are done on using well‐defined clinical and dosimetry quality measures defined by disease site experts in radiation oncology. HINGE application software connects seamlessly to the local IT/medical infrastructure via interfaces and cloud services and performs data extraction and aggregation functions without human intervention. It provides tools to assess variations in radiation oncology practices and outcomes and determines gaps in radiotherapy quality delivered by each provider.</description><identifier>ISSN: 1526-9914</identifier><identifier>EISSN: 1526-9914</identifier><identifier>DOI: 10.1002/acm2.13308</identifier><identifier>PMID: 34101349</identifier><language>eng</language><publisher>HOBOKEN: Wiley</publisher><subject>Automation ; Big Data ; big data in radiation oncology ; Cancer patients ; Cancer therapies ; Care and treatment ; Computational linguistics ; Data analysis ; Dictionaries ; Electronic health records ; Health care policy ; Informatics ; Information sharing ; Information storage and retrieval ; Language processing ; Life Sciences & Biomedicine ; Lung cancer ; Medical care ; Medical records ; Natural language interfaces ; Oncology ; Ontology ; Patients ; Planning ; Predictive analytics ; Prostate ; Quality management ; quality surveillance ; R&D ; Radiation ; Radiation Oncology Physics ; Radiation therapy ; Radiology, Nuclear Medicine & Medical Imaging ; Radiotherapy ; Research & development ; Science & Technology ; Simulation ; Software ; Surveillance</subject><ispartof>Journal of Applied Clinical Medical Physics, 2021-07, Vol.22 (7), p.177-187</ispartof><rights>2021 The Authors. published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.</rights><rights>COPYRIGHT 2021 John Wiley & Sons, Inc.</rights><rights>2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). 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We present the design and implementation framework of an integrated data ion, aggregation, and storage, curation, and analytics software: the Health Information Gateway and Exchange (HINGE), which collates data for cancer patients receiving radiotherapy. The HINGE software s structured DICOM‐RT data from the treatment planning system (TPS), treatment data from the treatment management system (TMS), and clinical data from the electronic health records (EHRs). HINGE software has disease site‐specific “Smart” templates that facilitate the entry of relevant clinical information by physicians and clinical staff in a discrete manner as part of the routine clinical documentation. Radiotherapy data ed from these disparate sources and the smart templates are processed for quality and outcome assessment. The predictive data analyses are done on using well‐defined clinical and dosimetry quality measures defined by disease site experts in radiation oncology. HINGE application software connects seamlessly to the local IT/medical infrastructure via interfaces and cloud services and performs data extraction and aggregation functions without human intervention. It provides tools to assess variations in radiation oncology practices and outcomes and determines gaps in radiotherapy quality delivered by each provider.</description><subject>Automation</subject><subject>Big Data</subject><subject>big data in radiation oncology</subject><subject>Cancer patients</subject><subject>Cancer therapies</subject><subject>Care and treatment</subject><subject>Computational linguistics</subject><subject>Data analysis</subject><subject>Dictionaries</subject><subject>Electronic health records</subject><subject>Health care policy</subject><subject>Informatics</subject><subject>Information sharing</subject><subject>Information storage and retrieval</subject><subject>Language processing</subject><subject>Life Sciences & Biomedicine</subject><subject>Lung cancer</subject><subject>Medical care</subject><subject>Medical records</subject><subject>Natural language interfaces</subject><subject>Oncology</subject><subject>Ontology</subject><subject>Patients</subject><subject>Planning</subject><subject>Predictive analytics</subject><subject>Prostate</subject><subject>Quality management</subject><subject>quality surveillance</subject><subject>R&D</subject><subject>Radiation</subject><subject>Radiation Oncology Physics</subject><subject>Radiation therapy</subject><subject>Radiology, Nuclear Medicine & Medical Imaging</subject><subject>Radiotherapy</subject><subject>Research & development</subject><subject>Science & 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Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Journal of Applied Clinical Medical Physics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kapoor, Rishabh</au><au>Sleeman, William C.</au><au>Nalluri, Joseph J.</au><au>Turner, Paul</au><au>Bose, Priyankar</au><au>Cherevko, Andrii</au><au>Srinivasan, Sriram</au><au>Syed, Khajamoinuddin</au><au>Ghosh, Preetam</au><au>Hagan, Michael</au><au>Palta, Jatinder R.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Automated data abstraction for quality surveillance and outcome assessment in radiation oncology</atitle><jtitle>Journal of Applied Clinical Medical Physics</jtitle><stitle>J APPL CLIN MED PHYS</stitle><date>2021-07</date><risdate>2021</risdate><volume>22</volume><issue>7</issue><spage>177</spage><epage>187</epage><pages>177-187</pages><issn>1526-9914</issn><eissn>1526-9914</eissn><abstract>Rigorous radiotherapy quality surveillance and comprehensive outcome assessment require electronic capture and automatic ion of clinical, radiation treatment planning, and delivery data. We present the design and implementation framework of an integrated data ion, aggregation, and storage, curation, and analytics software: the Health Information Gateway and Exchange (HINGE), which collates data for cancer patients receiving radiotherapy. The HINGE software s structured DICOM‐RT data from the treatment planning system (TPS), treatment data from the treatment management system (TMS), and clinical data from the electronic health records (EHRs). HINGE software has disease site‐specific “Smart” templates that facilitate the entry of relevant clinical information by physicians and clinical staff in a discrete manner as part of the routine clinical documentation. Radiotherapy data ed from these disparate sources and the smart templates are processed for quality and outcome assessment. The predictive data analyses are done on using well‐defined clinical and dosimetry quality measures defined by disease site experts in radiation oncology. HINGE application software connects seamlessly to the local IT/medical infrastructure via interfaces and cloud services and performs data extraction and aggregation functions without human intervention. It provides tools to assess variations in radiation oncology practices and outcomes and determines gaps in radiotherapy quality delivered by each provider.</abstract><cop>HOBOKEN</cop><pub>Wiley</pub><pmid>34101349</pmid><doi>10.1002/acm2.13308</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0003-3880-5886</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Automation Big Data big data in radiation oncology Cancer patients Cancer therapies Care and treatment Computational linguistics Data analysis Dictionaries Electronic health records Health care policy Informatics Information sharing Information storage and retrieval Language processing Life Sciences & Biomedicine Lung cancer Medical care Medical records Natural language interfaces Oncology Ontology Patients Planning Predictive analytics Prostate Quality management quality surveillance R&D Radiation Radiation Oncology Physics Radiation therapy Radiology, Nuclear Medicine & Medical Imaging Radiotherapy Research & development Science & Technology Simulation Software Surveillance |
| title | Automated data abstraction for quality surveillance and outcome assessment in radiation oncology |
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